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Relevant bibliographies by topics / Bernoulli-Gaussian

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Academic literature on the topic 'Bernoulli-Gaussian'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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Journal articles on the topic "Bernoulli-Gaussian"

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Hrabovets, Anastasiia. "Feynman diagrams and their limits for Bernoulli noise." Theory of Stochastic Processes 27(43), no. 1 (2023): 11–30. http://dx.doi.org/10.3842/tsp-4311781209-33.

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In this article, we will construct an approximation of Gaussian white noise based on the sequence of Bernoulli random variables and define Wick products and the stochastic exponent for the Bernoulli case. Here we will propose a method to calculate the expectations of Wick products for Bernoulli noise using diagrams, that converge to Feynman diagrams in the Gaussian case. We will prove that orthogonal polynomials for Bernoulli noise converge to Hermite polynomials, which form an orthogonal system in the Gaussian case.

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Törő, Olivér, Tamás Bécsi, Szilárd Aradi, and Péter Gáspár. "IMM Bernoulli Gaussian Particle Filter." IFAC-PapersOnLine 51, no. 22 (2018): 274–79. http://dx.doi.org/10.1016/j.ifacol.2018.11.554.

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Xie, Shaohao, Shaohua Zhuang, and Yusong Du. "Improved Bernoulli Sampling for Discrete Gaussian Distributions over the Integers." Mathematics 9, no. 4 (2021): 378. http://dx.doi.org/10.3390/math9040378.

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Discrete Gaussian sampling is one of the fundamental mathematical tools for lattice-based cryptography. In this paper, we revisit the Bernoulli(-type) sampling for centered discrete Gaussian distributions over the integers, which was proposed by Ducas et al. in 2013. Combining the idea of Karney’s algorithm for sampling from the Bernoulli distribution Be−1/2, we present an improved Bernoulli sampling algorithm. It does not require the use of floating-point arithmetic to generate a precomputed table, as the original Bernoulli sampling algorithm did. It only needs a fixed look-up table of very s

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Finamore, Weiler, Marcelo Pinho, Manish Sharma, and Moises Ribeiro. "Modeling Noise as a Bernoulli-Gaussian Process." Journal of Communication and Information Systems 38 (2023): 175–86. http://dx.doi.org/10.14209/jcis.2023.20.

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Transmission medium is always perturbed by noise with a random nature which can be characterized by taking a sequence of noise samples and, after analyzing the sequence, attributing a probabilistic model to represent the randomness of the noise. If thermal noise (receiver generated) is the only noise impairing the transmission (our only focus is digital transmission) the memoryless stationary discrete-time Gaussian process is the best model to probabilistically represent the noise. The mathematical representation of the transmission medium in such a situation yields the well known Gaussian Cha

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De La Rue, Thierry. "Systèmes dynamiques gaussiens d'entropie nulle, lâchement et non lâchement Bernoulli." Ergodic Theory and Dynamical Systems 16, no. 2 (1996): 379–404. http://dx.doi.org/10.1017/s0143385700008865.

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AbstractWe construct two real Gaussian dynamical systems of zero entropy; the first one is not loosely Bernoulli, and the second is a loosely Bernoulli Gaussian-Kronecker system. To get loose-Bernoullicity for the second system, we prove and use a property of planar Brownian motion on [0, 1]: we can recover the whole trajectory knowing only some angles formed by the motion.

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Bobkov, Sergey G., Friedrich Gotze, and Christian Houdre. "On Gaussian and Bernoulli Covariance Representations." Bernoulli 7, no. 3 (2001): 439. http://dx.doi.org/10.2307/3318495.

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Lavielle, Marc. "Bayesian deconvolution of Bernoulli-Gaussian processes." Signal Processing 33, no. 1 (1993): 67–79. http://dx.doi.org/10.1016/0165-1684(93)90079-p.

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Thoriq Nurchaidir, Widodo, and Bambang Prasetya Adhi. "KLASIFIKASI GENRE MUSIK MENGGUNAKAN ALGORITMA NAÏVE BAYES CLASSIFIER UNTUK LAYANAN STREAMING YOUTUBE." PINTER : Jurnal Pendidikan Teknik Informatika dan Komputer 7, no. 1 (2023): 1–6. http://dx.doi.org/10.21009/pinter.7.1.1.

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Genre musik adalah cara yang paling umum digunakan untuk mengorganisasi database musik digital. Klasifikasi merupakan pengelompokan data menjadi beberapa bagian yang telah ditentukan sehingga dapat mempermudah pengelolaan dan pencarian file musik bedasarkan genre musiknya. Penelitian ini melakukan klasifikasi genre musik untuk layanan streaming youtube. Metode penelitian yang digunakan pada penelitian ini adalah penelitian eksperimen laboratorium. Model pada penelitian mengklasifikasikan file musik menggunakan 3 jenis Algoritma Naïve Bayes Classifier yaitu: Gaussian Naïve Bayes, Bernoulli Naïv

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Al-Zuhairi, Dheyaa T., and Abbas Salman Hameed. "DOA estimation under Bernoulli-Gaussian impulsive noise." IOP Conference Series: Materials Science and Engineering 1090, no. 1 (2021): 012096. http://dx.doi.org/10.1088/1757-899x/1090/1/012096.

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Chong-Yung Chi and J. Mendel. "Viterbi algorithm detector for Bernoulli-Gaussian processes." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 3 (1985): 511–19. http://dx.doi.org/10.1109/tassp.1985.1164580.

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Dissertations / Theses on the topic "Bernoulli-Gaussian"

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Gaerke, Tiffani M. "Characteristic Functions and Bernoulli-Gaussian Impulsive Noise Channels." University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1408040080.

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Vu, Hung Van. "Capacities of Bernoulli-Gaussian Impulsive Noise Channels in Rayleigh Fading." University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1407370145.

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Boudineau, Mégane. "Vers la résolution "optimale" de problèmes inverses non linéaires parcimonieux grâce à l'exploitation de variables binaires sur dictionnaires continus : applications en astrophysique." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30020/document.

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Cette thèse s'intéresse à la résolution de problèmes inverses non linéaires exploitant un a priori de parcimonie ; plus particulièrement, des problèmes où les données se modélisent comme la combinaison linéaire d'un faible nombre de fonctions non linéaires en un paramètre dit de " localisation " (par exemple la fréquence en analyse spectrale ou le décalage temporel en déconvolution impulsionnelle). Ces problèmes se reformulent classiquement en un problème d'approximation parcimonieuse linéaire (APL) en évaluant les fonctions non linéaires sur une grille de discrétisation arbitrairement fine du

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Barbault, Pierre. "Un ticket pour le sparse : de l'estimation des signaux et des paramètres en problèmes inverses bernoulli-gaussiens." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG049.

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L'imagerie par Magnéto/Électro Encéphalographie (M/EEG) peut servir à reconstruire les foyers d'activité cérébrale en mesurant le champ Électro Magnétique produit par ce dernier. Même si le temps caractéristique des signaux enregistrés est assez faible pour pouvoir envisager un modèle linéaire d'acquisition, le nombre de sources possibles reste très large face au nombre de capteurs. De fait, il s'agit là d'un problème mal posé et de surcroît de grande dimension. Afin de se ramener dans le cadre d'un problème qui admet une hypothèse courante, et qui fait sens pour les neurones, est que les sour

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Yu, Jia. "Distributed parameter and state estimation for wireless sensor networks." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/28929.

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The research in distributed algorithms is linked with the developments of statistical inference in wireless sensor networks (WSNs) applications. Typically, distributed approaches process the collected signals from networked sensor nodes. That is to say, the sensors receive local observations and transmit information between each other. Each sensor is capable of combining the collected information with its own observations to improve performance. In this thesis, we propose novel distributed methods for the inference applications using wireless sensor networks. In particular, the efficient algor

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De, la Rue Thierry. "Quelques résultats sur les systèmes dynamiques gaussiens réels." Rouen, 1994. https://tel.archives-ouvertes.fr/tel-01546012.

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Le premier chapitre de cette thèse établit que l'entropie d'un système dynamique gaussien est soit nulle, soit infinie, suivant respectivement que sa mesure spectrale est singulière ou non par rapport à la mesure de Lebesgue. Ce résultat est étendu au cas d'une action multidimensionnelle. Dans le second chapitre, on développe un nouveau modèle pour les systèmes gaussiens, qui sont vus comme une transformation de la trajectoire brownienne plane. Cette transformation peut être insérée dans un flot, pour lequel on calcule un mouvement moyen. Ce modèle est utilisé dans le troisième chapitre pour c

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Guan, Feng-Bo, and 馮博冠. "Parameter Estimations of Condition Gaussian Distribution Given Bernoulli Distribution." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/87455161476925419316.

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碩士<br>國立中央大學<br>數學研究所<br>90<br>Abstract The sample mean and sample variance of a Gaussian distribution have the following nice statistical properties:(1)both are sufficient,(2)they are independent, (3)sample mean is m.l.e., UMVUE, and method of momemt estimator,(4)sample variance is m.l.e.,method of moment estimator and UMVUE if multiplied by a constant, (5)both estimators have variances achieve the Cramer-Rao lower bound,(6)both estimators are asymptotically efficient.Based on sample obtianed from the conditional Gaussian distribution given Bernoulli distribution,we study conditional sample

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"Bayesian Inference Frameworks for Fluorescence Microscopy Data Analysis." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.53545.

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abstract: In this work, I present a Bayesian inference computational framework for the analysis of widefield microscopy data that addresses three challenges: (1) counting and localizing stationary fluorescent molecules; (2) inferring a spatially-dependent effective fluorescence profile that describes the spatially-varying rate at which fluorescent molecules emit subsequently-detected photons (due to different illumination intensities or different local environments); and (3) inferring the camera gain. My general theoretical framework utilizes the Bayesian nonparametric Gaussian and beta-Bernou

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FANTACCI, CLAUDIO. "Distributed multi-object tracking over sensor networks: a random finite set approach." Doctoral thesis, 2015. http://hdl.handle.net/2158/1003256.

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The aim of the present dissertation is to address distributed tracking over a network of heterogeneous and geographically dispersed nodes (or agents) with sensing, communication and processing capabilities. Tracking is carried out in the Bayesian framework and its extension to a distributed context is made possible via an information-theoretic approach to data fusion which exploits consensus algorithms and the notion of Kullback–Leibler Average (KLA) of the Probability Density Functions (PDFs) to be fused. The first step toward distributed tracking considers a single moving object. Consensus

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Book chapters on the topic "Bernoulli-Gaussian"

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Cho, KyungHyun, Alexander Ilin, and Tapani Raiko. "Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21735-7_2.

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Ershov, M. A., and A. S. Voroshilov. "UCB Strategy for Gaussian and Bernoulli Multi-armed Bandits." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43257-6_6.

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Li, Ziqiang, Xun Cai, and Ti Liang. "Gaussian-Bernoulli Based Convolutional Restricted Boltzmann Machine for Images Feature Extraction." In Neural Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46672-9_66.

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Veziroğlu, Merve, Erkan Eziroğlu, and İhsan Ömür Bucak. "PERFORMANCE COMPARISON BETWEEN NAIVE BAYES AND MACHINE LEARNING ALGORITHMS FOR NEWS CLASSIFICATION." In Bayesian Inference - Recent Trends. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1002778.

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The surge in digital content has fueled the need for automated text classification methods, particularly in news categorization using natural language processing (NLP). This work introduces a Python-based news classification system, focusing on Naive Bayes algorithms for sorting news headlines into predefined categories. Naive Bayes is favored for its simplicity and effectiveness in text classification. Our objective includes exploring the creation of a news classification system and evaluating various Naive Bayes algorithms. The dataset comprises BBC News headlines spanning technology, busine

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Sivia, D. S. "Assigning probabilities." In Data Analysis. Oxford University PressOxford, 2006. http://dx.doi.org/10.1093/oso/9780198568315.003.0005.

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Abstract In the preceding three chapters, we have seen how pdfs can be manipulated, with the sum and product rule of probability, to address data analysis problems; little was said, however, about their assignment in the first place. We now turn to this basic question. In addition to justifying the earlier use of the binomial, Gaussian and Poisson distributions, we will have our first encounter with the principle of maximum entropy. In Section 1.2, we outlined how Cox showed that any method of plausible reasoning, which satisfies elementary requirements of logical consistency, must be equivale

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Conference papers on the topic "Bernoulli-Gaussian"

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Chen, Zhijin, Branko Ristic, and Du Yong Kim. "A Comparative Study of Bernoulli Gaussian-max Filter and Bernoulli Gaussian-sum Filter." In 2024 13th International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE, 2024. https://doi.org/10.1109/iccais63750.2024.10814535.

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Jones, George, and Ángel F. García-Fernández. "GOSPA-Driven Multi-Bernoulli Gaussian Sensor Management." In 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2024. http://dx.doi.org/10.1109/mfi62651.2024.10705781.

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Wei, Shaoxiu, Mingchao Liang, Bhaskar Rao, and Florian Meyer. "Bernoulli-Gaussian Scale Mixture Model and BP Method for Multi-Snapshot Sparse Signal Recovery." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889991.

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Sharma, Nipun, Swati Sharma, Asif Mohamed H. B, and Gnanaprakash V. "Comparative Analysis of Gaussian Naïve Bayes with Bernoulli Naïve Bayes for Guitar Chord Classification." In 2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2024. https://doi.org/10.1109/pdgc64653.2024.10984158.

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Cho, Kyung Hyun, Tapani Raiko, and Alexander Ilin. "Gaussian-Bernoulli deep Boltzmann machine." In 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas). IEEE, 2013. http://dx.doi.org/10.1109/ijcnn.2013.6706831.

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Jones, George, Ángel F. García-Fernández, and Prudence W. H. Wong. "GOSPA-Driven Gaussian Bernoulli Sensor Management." In 2023 26th International Conference on Information Fusion (FUSION). IEEE, 2023. http://dx.doi.org/10.23919/fusion52260.2023.10224220.

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Liu, Lei, Chongwen Huang, Yuhao Chi, Chau Yuen, Yong Liang Guan, and Ying Li. "Sparse Vector Recovery: Bernoulli-Gaussian Message Passing." In 2017 IEEE Global Communications Conference (GLOBECOM 2017). IEEE, 2017. http://dx.doi.org/10.1109/glocom.2017.8254836.

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Vila, Jeremy, and Philip Schniter. "Expectation-maximization Bernoulli-Gaussian approximate message passing." In 2011 45th Asilomar Conference on Signals, Systems and Computers. IEEE, 2011. http://dx.doi.org/10.1109/acssc.2011.6190117.

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Bazot, Cecile, Nicolas Dobigeon, Jean-Yves Tourneret, and Alfred O. Hero. "A Bernoulli-Gaussian model for gene factor analysis." In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947728.

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Bassi, Francesca, Michel Kieffer, and Cagatay Dikici. "Multiterminal source coding of Bernoulli-Gaussian correlated sources." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960130.

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